A CBR-Based Approach for Ship Collision Avoidance

Authors:
Yuhong Liu;Chunsheng Yang;Xuanmin Du
Affiliations:
Merchant Marine College of Shanghai Maritime University, Shanghai, China 200135;Institute for Information Technology, National Research Council, Canada;Shanghai Marine Electronic Equipment Research Institute, Shanghai, China 201108
Venue:
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
Year:
2008

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Abstract

In this paper, we propose a novel CBR-based approach for ship collision avoidance. After the introduction of the CBR-based decision-making support, we present two abstraction principles, selecting view points and describing granularity, to create collision avoidance cases from real-time navigation data. Several issues related case creation and CBR-based decision-making support are discussed in details, including case presentation, case retrieval and case learning. Some experimental results show the usefulness and applicability of CBR-based approach for ship collision avoidance.